Computer-Implemented Method and a System for Guiding a Vehicle Within a Scenario with Obstacles

ABSTRACT

A method and a system for guiding a vehicle from a source to a target within a scenario with obstacles are disclosed. The source is established as a starting point and a subpath to the target is computed. When an obstacle is detected that crosses the computed subpath, a plurality of obstacle-free subpaths are computed to connect the starting point to a waypoint of an outer boundary of a detected obstacle. Priorities for each waypoint are computed and ordered accordingly in a list of potential waypoints to select the highest priority waypoint in the list as a new starting point. Previous operations are repeated until the target is reached, thus the path is obtained by backtracking waypoints to the source. The vehicle can be then guided and the target reached via the obtained path.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to EP application number16382458.4, filed on Oct. 6, 2016, the entire contents of which areherein incorporated by reference.

FIELD

The present disclosure generally teaches techniques related to pathfinding in static and dynamic environments. In particular, the teachingsfurther relate planning and real-time guidance applications, includingthose of unmanned aerial vehicles (UAVs), ground robots and other movingautonomous vehicles.

BACKGROUND

A moving autonomous vehicle typically includes some type of sensorsystem for gathering information of an environment through which thevehicle navigates. A problem associated with guiding a vehicle is thelarge amount of computing power required to analyze the scenario inorder to identify obstacles and to avoid them in a reliable manner whilefinding a valid path to a target or destination. The interpretation ofdata is normally too slow to be useful within a scenario with obstaclesin real-time applications.

In general, the term path finding relates to a collection of techniquesfor finding an optimal (e.g. safest, shortest, etc.) route between twopoints, namely a source and a target, within a scenario withinobstacles. A visibility graph is a roadmap, a route that stores thevisibility relationships between a set of objects, considering asobstacles normally defined by polygons. The visibility graph isbasically made up of visible nodes and weighted edges. Visible nodesrepresent safe potential waypoints that are mutually connected by edges.Each edge carries a weight defined by a cost function, which is usuallythe Euclidean distance between the nodes. The joint of different edgesprovides a subpath. Thus a subpath defines a way of reaching a waypointfrom another waypoint. An optimal path may comprise several subpaths.The creation of a visibility graph is a complex task that usuallyrequires high computational times to provide a feasible response.

Normally visibility-based path finding approaches represent obstacles bygeometric figures (e.g. polygons, circles, etc.) since there are noperformance losses related to discretization. There are improvements insophisticated algorithms that make certain simplifications to build agraph, providing, in particular cases, good performance from thecomputational complexity point of view (e.g. N log N). Yet they considerall the polygons vertices of the scenario as potential waypoints. Inaddition, they are complex and unintuitive to implement.

In sum, existing approaches build a full graph regardless of theimportance of the information of different areas with respect to asource (e.g. the current position) and a target position. As aconsequence, they are extremely inefficient in real-time applicationsand complex environments.

For this reason, known solutions cannot be applied in practice, whenobstacles on the travel path are found and quick evasive maneuvers arerequired. An efficient generation under real-time constraints of a paththat is free of obstacles is therefore needed.

SUMMARY

As mentioned, the construction of traditional visibility graphs isinefficient for moderately intricate scenarios. To overcome or at leastmitigate some of the existing limitations, the disclosed teachingsprovide new techniques to carry out fewer checks.

In particular, the present disclosure is aimed at a computer-implementedmethod and a system for an efficient identification of a valid pathbased on a distinct construction of visibility graphs with an efficientfiltering of information. Preferably a geometrical (e.g. polygonal)description of the environment is used to increase the accuracy of theresulting path and to improve robustness and efficiency in contrast withexisting grid-based proposals. Pruning strategies are proposed to allowfiltering out non-essential information usually provided in real-worldoperational environments and to prioritize a list of candidate waypointsto reach the target.

As a consequence, a significant reduction of unnecessary waypoints andsubpaths can be achieved while such reduction advantageously does notinfluence the result. Thus, the obtained solution is guaranteed tocontain the optimal path that avoids the obstacles.

In this regard, the concept of obstacle should be construed in a broadsense. For instance, a no-incursion zone like a restricted area shouldbe considered an obstacle. Clearly, a physical object is considered anobstacle as well. The present disclosure also uses the common tangentconcept of a geometrical figure, normally a polygon. It is to beunderstood that a common tangent is a line touching the geometricalfigure such that the whole figure lies to the same side of the line. Incase of polygons, finding common tangents can be viewed as findingextreme vertices of the polygon. Such representation may simplifyimplementation.

In general terms, the present disclosure is a computer-implementedmethod for guiding a vehicle from a source to a target within a scenariowith obstacles. In order to find a valid path around obstacles, themethod firstly establishes the source as a starting point and computes asubpath from the starting point to the target. It detects when anobstacle crosses the computed subpath and repeatedly, for a detectedobstacle, this obstacle that must be avoided, being the firstencountered when traversing the subpath, the method computes a pluralityof obstacle-free subpaths avoiding the detected obstacle, eachobstacle-free subpath connecting the starting point to a waypoint of anouter boundary of a detected obstacle. The method further computes apriority value for each waypoint, wherein the priority value is computedbased on the distance between the waypoint and the target, assumingthere are no obstacles, and the accumulated distance from the source tothe waypoint. The accumulated distance is a sum of distances ofobstacle-free subpaths connecting the source to the waypoint. Suchdistance is defined according to a pre-established metric. The methodalso stores each waypoint and its corresponding priority in a list ofpotential waypoints (LPW) and then it selects the highest prioritywaypoint in the list as a new starting point for the next iteration.This is repeated until the target is reached. After this step, a validpath is obtained by backtracking waypoints from the target to thesource. Lastly, the method guides the vehicle according to the obtainedpath avoiding obstacles. This path is also optimal according to themetric.

In order to build the list, a priority value is associated to awaypoint, the priority of a given waypoint is based on a distancedefined according to a pre-established metric. The priority of awaypoint is computed as the distance between that waypoint and thetarget (regardless eventual obstacles) and the accumulated distance fromthe source to said waypoint.

The accumulated distance from the source to a waypoint may include atleast the distance associated to the obstacle-free subpath with itsending point being the waypoint. In case of the source and the waypointbe connected through several obstacle-free subpaths, the accumulateddistance also includes preceding obstacle-free subpaths.

In accordance with the present disclosure, the computer-implementedmethod for guiding a vehicle may be coded as a program product to beexecuted by a processor.

In accordance with the present disclosure, there is also provided asystem for guiding a vehicle from a source to a target within a scenariowith obstacles. The system includes a path computing unit thatestablishes the source as a starting point and computes a subpath fromthe starting point to the target. The system also includes a detectingunit that detects if an obstacle crosses a computed subpath. Then, thepath computing unit repeatedly computes a plurality of obstacle-freesubpaths avoiding a detected obstacle. Each obstacle-free subpathconnects the starting point to a waypoint of an outer boundary of anobstacle detected in a computed path. The system further includes aselecting unit that computes a priority value for each waypoint, it alsostores each waypoint and its corresponding priority in a LPW and thenselects the highest priority waypoint in the LPW as a starting point.The system repeats the previous actions until the target is reached. Thepath computing unit obtains a valid path by backtracking waypoints fromthe target to the source. A path a guiding unit also included in thesystem safely guides the vehicle according to the valid path. This pathis also optimal according to the metric.

The detecting may include ultrasonic detectors, mechanical contactdevices, laser ranging apparatus or a camera and an image processor. Itmay gather environment information from a Geographic Information System(GIS) or from a No Incursion Zones (NIZs) provider in order to detectobstacles.

Advantageous developments of the method and system are as follows.

BRIEF DESCRIPTION OF THE DRAWINGS

A series of drawings which aid in better understanding the disclosureand which are presented as non-limiting examples and are very brieflydescribed below.

FIG. 1 is a high-level block diagram of the disclosed teachings.

FIG. 2 schematically shows a flow diagram describing operations forseeking a route avoiding obstacles.

FIG. 3 shows a comparison of different algorithms to generate subpaths.

FIG. 4 shows a further comparison for generating common tangents assubpaths for avoidance of non convex obstacle.

FIGS. 5A, 5B, 5C, 5D, 5E and 5F depict several stages of the operationwithin a two-obstacle scenario.

DETAILED DESCRIPTION

Various embodiments illustrate the efficient creation of a valid path inscenarios within several obstacles (O) that can be used in a vehicle tonavigate and move autonomously. Importantly, the application of theseteachings is not limited to the particulars set forth in the followingdetailed description or drawings.

To avoid unnecessarily obscuring the understanding of these teachings,polygons are selected herein as the geometric representation to modelobstacles boundaries.

A list of potential waypoints (LPW) stores the polygons vertices (nodes)that are traversed to build a valid path with several subpaths. The LPWis a data storage device to be used as a dynamic container that iscontinuously updated. In an initialization step, the LPW has a singlewaypoint (W) which coincides with the source (S). An advantageousselection of the next waypoint to visit based on a heuristic (DirectedSearch) is devised. A graph is consistently expanded with feasible andoptimal sub-paths, thus reducing the trial-and-error iterative processand ensuring optimality.

The heuristic used as a decision-making identifies the most promisingwaypoint to build an optimal path with several subpaths according to apre-established metric. The metric defines a distance to measure thecost of traversed subpaths. This distance is normally the Euclideandistance, however other metrics may be used like Geodesic distance, lessfuel consumption distance, less altitude distance, etc. The metric to beused depends on the application. At any event, the heuristic isguaranteed never to overestimate the actual cost of thesource-waypoint-target path because the actual distance can either bethe length of the source-waypoint-target path or greater if there is aneventual obstacle between the waypoint and the target. This circumstancecan be seen in some of the figures.

When a potential waypoint from the LPW is obtained, the graph isexpanded through the branch (i.e. connected subpaths) that looks themost promising. Remarkably using the heurist does not necessarily implythe right node is going to be expanded, but in the worst-case scenarioall nodes would eventually be expanded. In other words, the applicationof this heuristic does not discard any waypoints but prioritizes them.

Remarkably, the path finding can be stopped whenever the target isreached for the first time. All the waypoints that remain as candidatesin the LPW are guaranteed to have a greater cost, even if a straightpath from them to the target is collision free. Therefore, the resultinggraph size is normally much smaller, which reduces computational timeand also possibly reduces memory and CPU usage, especially in largescenarios.

FIG. 1 is an exemplary and non-limiting description of a block diagramof a system on which the present disclosure can be implemented, inparticular in a UAV or in a ground robot.

A No-Incursion Zones (NIZs) Provider module 106 stores no-flight zonepolygons. This information may also be obtained from different sources.For instance, in a tactical scenario, a vehicle might have onboardsensors 102 to detect obstacles (O). In this case, the NIZs Providermodule 106 gets information from such sensors 102 and defines a NIZ thatrepresents the obstacle. In more strategic scenarios, the source to getinformation can be either a Geographic Information system (GIS) 104 or amap where, for instance, cities' boundaries can be identified by theNIZs Provider module 106 and then, modeled. In the present example, NIZsare modeled by polygons, however, any other geometric figure might beused to represent other real hazards. For instance, the uncertainty ofthe position of an obstacle (O) that represent a NIZ might be modeled bya circle or an ellipsoid.

A user or a Navigation system establishes the source (S) and target (T)which are provided to the path computing unit 110. For instance, theuser wants to identify the shortest path between the source (S) and thetarget (T). In other cases, a Navigation system of a vehicle providesthe current position of the vehicle that will be used as the source (S).

A path computing unit 110 is in charge of calculating a metric distance.In the present embodiment, distance calculations are based on theEuclidean distance but in other implementations might be calculatedbased on other figure of merits (e.g. fuel consumption, less altitude,etc.). In addition, the path computing unit 110 cooperates with aselecting unit 130 in charge of keeping updated a storage medium whereininformation regarding a list of potential waypoints (LPW) that may bechecked is stored. In order to do so, a two way communication with theselecting unit 130 is established.

The selecting unit 130 carries out the expansion of the graph based onthe distance calculations. It defines the segments (subpaths) betweenwaypoints and calculates their intersection with the polygons. Finally,the selecting unit 130 stores information of the calculated subpath andprovides an optimal path by means of a list of waypoints to a guidingunit 140. This unit 140 may control the vehicle to safely reach thetarget (T).

FIG. 2 shows a flow diagram according to the heuristic for seeking aroute to avoid obstacles represented by polygons. The main steps areexplained bellow:

1. The algorithm starts with an initialization step 202 for initializinga list of potential waypoints (LPW).

2. A getting step 204 gets a first waypoint (a vertex of a polygon) fromLPW. The most promising waypoint according to the metric—called S′—isidentified from the LPW in order to expand the graph. By construction,such waypoint is always located at the first position of the list.

3. A segment visibility check step 206, where S′T segment is checked tobe free of obstacles (polygons). S′ is the waypoint that is beingexpanded and T is the target waypoint. Then, the subpath defined as thesegment S′T is intersected with all the polygons. If the segment S′T isnot obstacle-free, the algorithm jumps to step 208.

4. Adding subpath step 222, where subpath S′T is added to the graphprovided it does not cross any obstacle, that is to say the segment S′Tis visible. When this step is reached, the heuristic (see metriccalculation step 214) assures that solving the current graph willprovide the problem's optimal solution without further expansion.

5. Identifying obstacles step 208. This is an efficiency-related stepthat reduces the complexity by selecting and only paying attention tothose obstacles that are obstructing a segment's visibility. Thus, thealgorithm is able to filter the obstacles whose inclusion is notnecessary as discussed in FIG. 3.

6. Calculating common tangent segment step 210. Common tangent (S′W) ofintersected polygons from S′ are determined. Instead of building a fullvisibility graph, obstacles are avoided by finding the common tangentsbetween S′ and the intersected polygons.

7. Common tangent segment visibility check step 212, where visibility oftangent segments (S′W) is determined. W represents any of the endpointsof the tangent segments. If a tangent segment is visible, the flowdiagram can continue through step 214. If, however, a tangent segmentS′W crosses any obstacle, the heuristic cannot add that segment to thegraph and identifies the obstacles that are involved in thatintersection by jumping to step 208.

8. Priority calculation step 214. Once this step has been reached, a setof visible segments are found which are tangent to those polygonsinvolved in the conflict. The heuristic uses a criterion to order theLPW in such a way that traversing the graph is more efficient than doingit following an arbitrary order.

Taking as input the ending points of the visible tangent segments(potential waypoints W), the minimum cost is calculated (e.g. inEuclidean distance) that visiting each waypoint could have.

9. Including endpoints step 216, where each waypoint W is included inthe LPW (ordered according to its corresponding priority). The prioritycalculation is based on the S′WT lengths, the corresponding waypoints(W) are inserted in the LPW by comparing their associated prioritieswith those that were previously added to the list. Thus, the LPWcontains the list of all the potential waypoints ordered according tothe distance S′WT. The heuristic is guaranteed never to overestimate theactual cost of the S′WT path because the actual distance can either bethe length of the S′WT path or greater, if there is an obstacle betweenwaypoint W and target T. In the worst-case scenario all nodes wouldeventually be expanded whereas, in other cases, the expansion can besignificantly reduced.

When the method gets a node to visit from the LPW (step 202), the graphis expanded through the branch that looks the most promising.

Using the heuristic criterion does not necessarily imply the right nodeis going to be expanded. In other words, using the heuristic does notdiscard any waypoints; it merely prioritizes them.

As a result of employing this heuristic, the graph building can bestopped whenever the target is reached for the first time. All the nodesthat remain in the LPW are guaranteed to have a greater cost (distance)even if a straight path from them to the target was collision free.Therefore, the resulting graph size is much smaller, which saves bothcomputer memory and processing requirements, especially in largescenarios. The example in FIG. 3 shows how the heuristic works with anexample of a static scenario.

10. In the adding step 218, S′W segments are added to the graph. All S′Ware now collision-free and can be included to the graph. However, thealgorithm checks if W has been reached before through a shorter path, inwhich case that S′W segment is not added to the graph, reducing thegraph's complexity. The same applies to the insertion of points in theLPW.

11. Removing step 220, where S′ is removed from the LPW. Having expandedthe graph from node S′, the node is removed from the list. For the nextiteration, the LPW will contain the vertices to visit that haveprogressively emerged from S′.

FIG. 3 shows an example of the complexity reduction. The dotted line isrepresentative of how the target is reached according to the presentteachings ignoring three obstacles 320, 330, 340 and only keeps one ofthem, namely obstacle 310 thus speeding up the finding of the optimal(shortest) path. In this specific scenario, there are 18 potentialwaypoints (16 vertices of polygons plus source and target). A naivevisibility graph algorithm would have generated a completegraph—represented as black segments—with 18 nodes and 71 edges(subpaths), whereas the present approach is able to obtain the shortestpath with 4 nodes and 3 edges, shown in dotted line. The thicker dottedsegments show connected subpaths that form a valid path from S 302 to T304.

FIG. 4 shows how using common tangents helps reduce the graphdramatically even for a single obstacle O modelled as a polygon. Asignificant reduction in the size of the graph leads to lowercomputation times. Note that the polygon's non-convexity doesn't affectthe algorithm because the shortest path between source S and target T isproved to consist of common tangent segments. The thicker segments showthe valid (e.g. shortest) path to reach T from S.

Reference is now made to FIGS. 5A to 5F, where a simple scenario ispresented including some numerical values for a better appreciation ofhow the present teachings help to efficiently find the optimal path.

The scenario basically includes a source S and target T and twoobstacles (O) modelled as polygons, a quadrilateral 510 with verticesABCD and a triangle 520 with vertices EFG.

To find a conflict-free shortest path amidst the polygons to reach thetarget T from the source S, the distance between S and T is calculated,which in this case is 10 m (from (0,−5) to (0,5)). Such distance alongwith the S starting point initialize the list of potential waypoints(LPW); so that the LPW initially contains the pair: ([10, S]).

Once the LPW has been initiated, the graph is expanded from the firstpoint in LPW (in this case, the S point. This task is illustrated inFIGS. 5A and 5B. The graph expansion is as follows:

-   -   i) The ST segment is defined and a detection of whether ST        crosses any polygon is carried out. In this case, the ST segment        collides with the obstacle 510 so that the square is the first        polygon to avoid.    -   ii) In order to avoid a polygon, the two common tangent segments        of the detected polygon are calculated: SC and SB tangent        segments are obtained.    -   iii) In an iterative manner, it is checked whether such common        tangent segments cross any other polygon within the scenario,        and repeats step ii until no segment crosses any of the        polygons. In the example, the tangent segment SC is not visible        because crosses the triangle obstacle 520. Thus, the triangle is        considered as a new polygon to avoid in order to reach the C        point. After step ii is applied between point S and the        triangle, two new common tangents are obtained: SE and SF        tangent segments.    -   iv) The new conflict-free segments are appended to the graph, in        this case the SB, SE and SF segments. Once they are added, a        priority value is assigned to each segment ending point (B, E        and F) and ordered according to the minimum value of the metric.        Such metric is computed in this example as the distance between        the source S point and the points to be appended to the graph,        plus the distance from the latter to the target T point assuming        it is free of obstacles; it has to be noted that the latter        distance (from the new points to T) does not have to be conflict        free necessarily. Referring back to the example, point B has a        metric associated of 10.206 (distance S-B+distance B-T); point F        has a metric associated of 10.059 (distance S-F+distance F-T)        and finally point E has a metric of 12.033 (distance        S-B+distance B-T). Finally, by expanding from S, the S point        from the LPWV is removed. Therefore, if we sort the new points        according to the metric, the graph of LPW is as follows:        ([10.059, F], [10.206, B], [12.033, E]).

On the FIG. 5C, the graph is expanded from the F node, which is thefirst point in the LPW. The heuristic distance associated to the F noderepresents that the path S→F→Target would be shorter than the path fromB if there were no additional obstacles. Now we are to expand the graphfrom the F point following the same exact procedure we just used whenexpanding from S, which would be:

-   -   i) The FT segment is defined and checked whether it intersects        any of the polygons in the scenario. In this case, the FT        segment intersects the square obstacle 510.    -   ii) The common tangents are generated which give rise to the        points C and B. As point B is already included in LPW it is        ignored in this step because it is a longer path towards T        through B than the one included in the previous graph expansion,        so we'll stick to C in this expansion. That is to say, the        length of the SFB path with the length of the SB path is        compared. In this case, the most efficient path to reach the        waypoint B is the SB path. Thus, the segment FB is not added to        the graph.    -   iii) It is checked whether FC intersects another polygon, which        it does not.    -   iv) The new conflict free segment is appended into the graph, in        this case the metric associated to C is the distance S-F-C-T        which gives 10.242. Again, since we are expanding from F, we        remove F from the list of LPWV which stays as follows after this        expansion: [10.206, B], [10.242, C], [12.033, E].

In FIG. 5D, the algorithm expands the graph from B because, according tothe heuristic, is the shortest potential path and consequently, it isthe first in the list. Again, the BT segment is defined and a newcollision with the square obstacle 510 is found. To avoid the squareobstacle 510 from B, its two common tangent segments (BC and BA) aredefined. As in the previous cases, there is a more efficient path in thegraph to reach the C point. Therefore, the BC segment is not added tothe graph. Nevertheless, the BA segment must be added to the graph. Thefirst point in LPW is deleted, it calculates the heuristic distance fromA and finally, it adds the BA segment. At the end of the fourth step,the LPW is as follows: [10.242, C], [10.246, A], [12.033, E].

In FIG. 5E, the algorithm expand the graph from C, which is the firstpoint in LPW. Following the explained logic, the segment CD is added tothe graph and the calculation of the heuristic process provides the nextlist to continue with the expansion of the graph: [10.246, A], [10.283,D], [12.033, E].

The last step is shown in FIG. 5F. In such step, the algorithm expandsthe graph from the A point (first point in the LPWs). In this case, thesegment AT is collision-free and reaches the target T point. Since thegraph is always expanded from the “best-looking” node, the graphexpansion can be stopped once the target T is reached.

The present teachings guarantee the resulting path (S->B->A->Target) canbe obtained effortlessly by backtracking the path from the target, whichsimplifies the problem to a great extent.

Several relevant features presented herein significantly reduce thecomputation cost to find autonomous shortest lateral paths regardingexisting solutions.

One of the preferred embodiments of the above teachings relates to amethod to be implemented in a computer system including one or moreprocessors. Another preferred embodiment relates to a system, like anavigation system or a guidance system to be implemented in anautonomous or semi-autonomous vehicle. Another preferred embodimentrelates to computer program product that includes code instructionsthat, when executed by a processor, causes the processor to perform themethods described herein. In particular, automatically guiding avehicle, like a UAV or a ground robot, from a source S to a target Twithin a scenario with obstacles.

1. A computer-implemented method for guiding a vehicle from a source (S)to a target (T) within a scenario with obstacles, the method comprising:establishing the source (S) as a starting point (S′); computing asubpath from the starting point (S′) to the target (T); detecting if anobstacle (O) crosses the computed subpath; repeatedly, for a detectedobstacle (O), computing a plurality of obstacle-free subpaths avoidingthe detected obstacle (O), each obstacle-free subpath connecting thestarting point (S′) to a waypoint (W) of an outer boundary of a detectedobstacle (O); computing a priority value for each waypoint (W), whereinthe priority value is computed based on: the distance between thewaypoint (W) and the target (T) and the accumulated distance from thesource to the waypoint (W), wherein accumulated distance comprises atleast the distance between the starting point and the ending point of atleast one computed obstacle-free subpath from the source to the waypoint(W), the distance being defined according to a pre-established metric;storing each waypoint (W) and its corresponding priority in a list ofpotential waypoints (LPW); selecting the highest priority waypoint (W)in the list as a starting point (S′) and repeating the previous stepsuntil the target (T) is reached; obtaining an optimal path bybacktracking waypoints (W) from the target (T) to the source (S); andguiding the vehicle according to the optimal path.
 2. Thecomputer-implemented method of claim 1, wherein the outer boundary of anobstacle (O) is modelled by a geometrical figure.
 3. Thecomputer-implemented method of claim 2, wherein the waypoint (W) isobtained as an intersection of a common tangent segment to thegeometrical figure corresponding to the obstacle (O).
 4. Thecomputer-implemented method of claim 3, wherein the waypoint is a vertexof a polygon.
 5. The computer-implemented method of claim 1, wherein themetric is selected among the following: Euclidean, less fuelconsumption, less altitude, Geodesic distance.
 6. Thecomputer-implemented method of claim 1, wherein the obstacle isrepresented by a no-incursion zone.
 7. The computer-implemented methodof claim 1, wherein the vehicle is selected among a UAV, an aircraft, aground robot and an autonomous car.
 8. A system for guiding a vehiclefrom a source (S) to a target (T) within a scenario with obstaclescomprising: a path computing unit (10) configured to: establish thesource (S) as a starting point (S′); compute a subpath from the startingpoint (S′) to the target (T); a detecting unit (20) configured to detectif an obstacle (O) crosses a computed subpath; the path computing unit(10) further configured to repeatedly compute a plurality ofobstacle-free subpaths avoiding the detected obstacle (O), eachobstacle-free subpath connecting the starting point (S′) to a waypoint(W) of an outer boundary of an obstacle (O) detected in a computed path;a selecting unit (30) configured to: compute a priority value for eachwaypoint (W), wherein the priority value is computed based on thedistance between the waypoint (W) and the target (T) and the accumulateddistance from the source to the waypoint (W), wherein accumulateddistance comprises at least the distance between the starting point andthe ending point of at least one generated obstacle-free path from thesource to the waypoint (W), the distance being defined according to apre-established metric; store each waypoint (W) and its correspondingpriority in a list of potential waypoints (LPW) to check; select thehighest priority waypoint (W) in the list as a starting point (S′) andrepeating the previous steps until the target (T) is reached; the pathcomputing unit (10) further configured to: obtain an optimal path bybacktracking waypoints (W) from the target (T) to the source (S); and aguiding unit (40) configured to guide the vehicle according to theoptimal path.
 9. The system of claim 8, wherein the outer boundary of anobstacle (O) is modelled by a geometrical figure.
 10. The system ofclaim 9, wherein the waypoint (W) is obtained as an intersection of acommon tangent segment to the geometrical figure corresponding to theobstacle (O).
 11. The system of claim 10, wherein the waypoint is avertex of a polygon.
 12. The system of claim 8, wherein the metric isselected among the following: Euclidean, less fuel consumption, lessaltitude, Geodesic distance.
 13. The system of claim 8, wherein theobstacle is represented by a no-incursion zone.
 14. The system of claim8, wherein the vehicle is selected among a UAV, an aircraft, a groundrobot and an autonomous car.